I implemented different machine learning algorithms on a matrix with binary data to predict a univariate target with two classes.
- random forest (accuracy = 62.01)
- Neural Network(acc= 58.9)
- svm-radial kernel (accuracy = 58.02)
- linear discriminant analysis(accuracy = 57.9)
- logistic regression(accuracy = 57.6).
My baseline accuracy is 52.55. But in case of Naive Bayes in same setting gives only 48.5 accuracy that identifies only one class in y. predict.
Is it possible for a machine learning model to behave worse than a random classification?